A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment
This work addresses video quality assessment for laparoscopic surgery, which is critical for medical imaging but is incremental as it applies existing neural network techniques to a specific domain.
The paper tackled the problem of assessing video quality in laparoscopic surgery, where distortions can hinder surgical performance, by proposing neural network-based methods for distortion classification and quality prediction, achieving results that outperformed recent conventional and deep learning approaches on a new database.
Video quality assessment is a challenging problem having a critical significance in the context of medical imaging. For instance, in laparoscopic surgery, the acquired video data suffers from different kinds of distortion that not only hinder surgery performance but also affect the execution of subsequent tasks in surgical navigation and robotic surgeries. For this reason, we propose in this paper neural network-based approaches for distortion classification as well as quality prediction. More precisely, a Residual Network (ResNet) based approach is firstly developed for simultaneous ranking and classification task. Then, this architecture is extended to make it appropriate for the quality prediction task by using an additional Fully Connected Neural Network (FCNN). To train the overall architecture (ResNet and FCNN models), transfer learning and end-to-end learning approaches are investigated. Experimental results, carried out on a new laparoscopic video quality database, have shown the efficiency of the proposed methods compared to recent conventional and deep learning based approaches.